Using Log Data And Machine Learning To Weed Out The Bad Guys

We have seen in the past how security threats often have their origins inside organizations. While high-profile data breaches from shady external characters create a dramatic story that is attractive to the media and Hollywood, there is arguably even greater risk from internal IP theft. Indeed, losses due to IP theft are estimated to be more than $300B each year. I recently came across a case study that involved an unnamed $20 billion manufacturer. The company, who for commercial sensitivity reasons obviously didn't want to be named, was recently a victim of internal IP theft, and spent a year and more than a million dollars working with a traditional security player trying to identify the rogue players.